This repo is developed for Strong Baseline For Vehicle Re-Identification in Track 2 Ai-City-2021 Challenges

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Deep Learning track2_aicity_2021
Overview

A STRONG BASELINE FOR VEHICLE RE-IDENTIFICATION

This paper is accepted to the IEEE Conference on Computer Vision and Pattern Recognition Workshop(CVPRW) 2021

This repo is the official implementation for the paper A Strong Baseline For Vehicle Re-Identification in Track 2, 2021 AI CITY CHALLENGE.

I.INTRODUCTION

Our proposed method sheds light on three main factors that contribute most to the performance, including:

  • Minizing the gap between real and synthetic data
  • Network modification by stacking multi heads with attention mechanism to backbone
  • Adaptive loss weight adjustment.

Our method achieves 61.34% mAP on the private CityFlow testset without using external dataset or pseudo labeling, and outperforms all previous works at 87.1% mAP on the Veri benchmark.

II. INSTALLATION

  1. pytorch>=1.2.0
  2. yacs
  3. apex (optional for FP16 training, if you don't have apex installed, please turn-off FP16 training by setting SOLVER.FP16=False)
$ git clone https://github.com/NVIDIA/apex
$ cd apex
$ pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
  1. python>=3.7
  2. cv2

III. REPRODUCE THE RESULT ON AICITY 2020 CHALLENGE

Download the pretrained checkpoint resnext101_ibn

1.Train

  • Vehicle ReID
    ./scripts/train.sh
  • Orientation ReID
    ./scripts/ReOriID.sh
  • Camera ReID
    ./scripts/ReCamID.sh

2. Test

    ./scripts/test.sh

IV. PERFORMANCE

1. Comparison with state-of-the art methods on VeRi776

Comments
  • The link of [checkpoint] are broken.

    The link of [checkpoint] are broken.

    Hi, I want to download the checkpoint, but the google link are broken. Could you update link? Thanks.

    1. Comparison with state-of-the art methods on VeRi776 Download the checkpoint
    opened by 402650294 2
  • Train result

    Train result

    excuse me, I train the model by myself base on the default veri.yml setting: backbone ResNeXt101 ibn a, multi-head, CE and SupCon losses, MALW and imagenet pretrained, but only reach 70% mAP, Is my parameter not set correctly?

    opened by Kim-Z 0
  • veri776

    veri776

    image I trained on veri. Why hasn't it worked? The accuracy rate hasn't been improved. I only modified the data set of the source code and the path of the pre training model. I hope to get an answer. Thank you!

    opened by CarrieYpi 1
  • Could you offer the feat_distmat.npy file of camera and orientation?

    Could you offer the feat_distmat.npy file of camera and orientation?

    Thanks for your work. And I have a question which is how to get the camera and orientation dismat, after reading your paper and VOC(Vehicle-Orientation-Camera) paper, I only find from VehicleX to get the dismat, but how to get the two files? I can not find the generated codes. Or could your offer the two file? Thanks a lot.

        ori_dist = np.load('./output/aicity20/0409-ensemble/ReOriID/feat_distmat.npy')
    opened by starstarb 1
  • tools/train.py

    tools/train.py

    Hello I'm trying to run the code that the paper provided.But I get the error in line 16 in tools/train.py(ModuleNotFoundError: No module named 'lib.config'). Can someone help me?

    opened by zooooooly 0
  • released checkpoint: veri_871.pth's results

    released checkpoint: veri_871.pth's results

    Hi, Thank you for your released code! I have some questions when testing with the released veri_871.pth.

    1. When tested without post-processing technique, I got mAP=78.7%;
    2. I can get the reported mAP 87.1% when using re-rank technique. But I thought it was clarified in the paper that non post-processing was used. "For a fair comparison, we only use single model, including backbone ResNeXt101 ibn a, multihead, CE and SupCon losses and MALW without applying pos-processing technique and synthetic data. We achieve the state-of-the-art performance with a large margin compared to previous works" (sec 4.4)
    3. Also I have reproduced the VOC-ReID repo: res50_ibn_a, which got the results on veri-776: 81.5% (w/o re-rank); 87.5% (with re-rank). It seems that larger backbone / multihead / MALW do not brought bonus here? If I have any misunderstandings, please help me to point out. Thank you very much. Attached are the testing logs. voc_reid_test_with_rerank.txt veri_871_test_with_rerank.txt
    opened by jiaohere 4
Owner
Cybercore Co. Ltd
Cybercore Co. Ltd
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